Free Statistics

of Irreproducible Research!

Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationFri, 20 Nov 2009 08:50:43 -0700
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2009/Nov/20/t12587326187mkmtdo1q5q3dmq.htm/, Retrieved Fri, 29 Mar 2024 07:58:06 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=58287, Retrieved Fri, 29 Mar 2024 07:58:06 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact140
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [Q1 The Seatbeltlaw] [2007-11-14 19:27:43] [8cd6641b921d30ebe00b648d1481bba0]
- RMPD  [Multiple Regression] [Seatbelt] [2009-11-12 13:54:52] [b98453cac15ba1066b407e146608df68]
-    D      [Multiple Regression] [WS 7: eerste model] [2009-11-20 15:50:43] [17b3de9cda9f51722106e41c76160a49] [Current]
-             [Multiple Regression] [WS 7: Model 1] [2009-11-20 16:54:33] [8cf9233b7464ea02e32be3b30fdac052]
Feedback Forum

Post a new message
Dataseries X:
423	114
427	116
441	153
449	162
452	161
462	149
455	139
461	135
461	130
463	127
462	122
456	117
455	112
456	113
472	149
472	157
471	157
465	147
459	137
465	132
468	125
467	123
463	117
460	114
462	111
461	112
476	144
476	150
471	149
453	134
443	123
442	116
444	117
438	111
427	105
424	102
416	95
406	93
431	124
434	130
418	124
412	115
404	106
409	105
412	105
406	101
398	95
397	93
385	84
390	87
413	116
413	120
401	117
397	109
397	105
409	107
419	109
424	109
428	108
430	107




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time3 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 3 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58287&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]3 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gwilym Jenkins' @ 72.249.127.135[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58287&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58287&T=0

As an alternative you can also use a QR Code:  

The GUIDs for individual cells are displayed in the table below:

Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time3 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135







Multiple Linear Regression - Estimated Regression Equation
Y[t] = + 315.014586098755 + 1.01436997434097X[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Y[t] =  +  315.014586098755 +  1.01436997434097X[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58287&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Y[t] =  +  315.014586098755 +  1.01436997434097X[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58287&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58287&T=1

As an alternative you can also use a QR Code:  

The GUIDs for individual cells are displayed in the table below:

Multiple Linear Regression - Estimated Regression Equation
Y[t] = + 315.014586098755 + 1.01436997434097X[t] + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)315.01458609875514.79719521.288800
X1.014369974340970.1210328.38100

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Ordinary Least Squares \tabularnewline
Variable & Parameter & S.D. & T-STATH0: parameter = 0 & 2-tail p-value & 1-tail p-value \tabularnewline
(Intercept) & 315.014586098755 & 14.797195 & 21.2888 & 0 & 0 \tabularnewline
X & 1.01436997434097 & 0.121032 & 8.381 & 0 & 0 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58287&T=2

[TABLE]
[ROW][C]Multiple Linear Regression - Ordinary Least Squares[/C][/ROW]
[ROW][C]Variable[/C][C]Parameter[/C][C]S.D.[/C][C]T-STATH0: parameter = 0[/C][C]2-tail p-value[/C][C]1-tail p-value[/C][/ROW]
[ROW][C](Intercept)[/C][C]315.014586098755[/C][C]14.797195[/C][C]21.2888[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]X[/C][C]1.01436997434097[/C][C]0.121032[/C][C]8.381[/C][C]0[/C][C]0[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58287&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58287&T=2

As an alternative you can also use a QR Code:  

The GUIDs for individual cells are displayed in the table below:

Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)315.01458609875514.79719521.288800
X1.014369974340970.1210328.38100







Multiple Linear Regression - Regression Statistics
Multiple R0.740084845814715
R-squared0.547725579004591
Adjusted R-squared0.539927744159842
F-TEST (value)70.2407257796007
F-TEST (DF numerator)1
F-TEST (DF denominator)58
p-value1.41233691408615e-11
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation17.8483841551159
Sum Squared Residuals18476.7593830185

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.740084845814715 \tabularnewline
R-squared & 0.547725579004591 \tabularnewline
Adjusted R-squared & 0.539927744159842 \tabularnewline
F-TEST (value) & 70.2407257796007 \tabularnewline
F-TEST (DF numerator) & 1 \tabularnewline
F-TEST (DF denominator) & 58 \tabularnewline
p-value & 1.41233691408615e-11 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 17.8483841551159 \tabularnewline
Sum Squared Residuals & 18476.7593830185 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58287&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.740084845814715[/C][/ROW]
[ROW][C]R-squared[/C][C]0.547725579004591[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.539927744159842[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]70.2407257796007[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]1[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]58[/C][/ROW]
[ROW][C]p-value[/C][C]1.41233691408615e-11[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]17.8483841551159[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]18476.7593830185[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58287&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58287&T=3

As an alternative you can also use a QR Code:  

The GUIDs for individual cells are displayed in the table below:

Multiple Linear Regression - Regression Statistics
Multiple R0.740084845814715
R-squared0.547725579004591
Adjusted R-squared0.539927744159842
F-TEST (value)70.2407257796007
F-TEST (DF numerator)1
F-TEST (DF denominator)58
p-value1.41233691408615e-11
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation17.8483841551159
Sum Squared Residuals18476.7593830185







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
1423430.652763173627-7.65276317362673
2427432.681503122308-5.68150312230792
3441470.213192172924-29.2131921729241
4449479.342521941993-30.3425219419928
5452478.328151967652-26.3281519676519
6462466.15571227556-4.15571227556016
7455456.01201253215-1.01201253215042
8461451.9545326347879.04546736521348
9461446.88268276308214.1173172369183
10463443.83957284005919.1604271599413
11462438.76772296835423.2322770316461
12456433.69587309664922.304126903351
13455428.62402322494426.3759767750559
14456429.63839319928526.3616068007149
15472466.155712275565.84428772443984
16472474.270672070288-2.27067207028795
17471474.270672070288-3.27067207028795
18465464.1269723268780.87302767312179
19459453.9832725834685.01672741653153
20465448.91142271176416.0885772882364
21468441.81083289137726.1891671086232
22467439.78209294269527.2179070573052
23463433.69587309664929.304126903351
24460430.65276317362629.3472368263739
25462427.60965325060334.3903467493969
26461428.62402322494432.3759767750559
27476461.08386240385514.9161375961447
28476467.1700822499018.82991775009886
29471466.155712275564.84428772443984
30453450.9401626604462.05983733955445
31443439.7820929426953.21790705730516
32442432.6815031223089.31849687769198
33444433.69587309664910.304126903351
34438427.60965325060310.3903467493969
35427421.5234334045575.4765665954427
36424418.4803234815345.51967651846562
37416411.3797336611484.62026633885244
38406409.350993712466-3.35099371246562
39431440.796462917036-9.7964629170358
40434446.882682763082-12.8826827630817
41418440.796462917036-22.7964629170358
42412431.667133147967-19.6671331479670
43404422.537803378898-18.5378033788983
44409421.523433404557-12.5234334045573
45412421.523433404557-9.5234334045573
46406417.465953507193-11.4659535071934
47398411.379733661148-13.3797336611476
48397409.350993712466-12.3509937124656
49385400.221663943397-15.2216639433968
50390403.26477386642-13.2647738664198
51413432.681503122308-19.681503122308
52413436.738983019672-23.7389830196719
53401433.695873096649-32.695873096649
54397425.580913301921-28.5809133019212
55397421.523433404557-24.5234334045573
56409423.552173353239-14.5521733532393
57419425.580913301921-6.5809133019212
58424425.580913301921-1.5809133019212
59428424.566543327583.43345667241977
60430423.5521733532396.44782664676075

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 423 & 430.652763173627 & -7.65276317362673 \tabularnewline
2 & 427 & 432.681503122308 & -5.68150312230792 \tabularnewline
3 & 441 & 470.213192172924 & -29.2131921729241 \tabularnewline
4 & 449 & 479.342521941993 & -30.3425219419928 \tabularnewline
5 & 452 & 478.328151967652 & -26.3281519676519 \tabularnewline
6 & 462 & 466.15571227556 & -4.15571227556016 \tabularnewline
7 & 455 & 456.01201253215 & -1.01201253215042 \tabularnewline
8 & 461 & 451.954532634787 & 9.04546736521348 \tabularnewline
9 & 461 & 446.882682763082 & 14.1173172369183 \tabularnewline
10 & 463 & 443.839572840059 & 19.1604271599413 \tabularnewline
11 & 462 & 438.767722968354 & 23.2322770316461 \tabularnewline
12 & 456 & 433.695873096649 & 22.304126903351 \tabularnewline
13 & 455 & 428.624023224944 & 26.3759767750559 \tabularnewline
14 & 456 & 429.638393199285 & 26.3616068007149 \tabularnewline
15 & 472 & 466.15571227556 & 5.84428772443984 \tabularnewline
16 & 472 & 474.270672070288 & -2.27067207028795 \tabularnewline
17 & 471 & 474.270672070288 & -3.27067207028795 \tabularnewline
18 & 465 & 464.126972326878 & 0.87302767312179 \tabularnewline
19 & 459 & 453.983272583468 & 5.01672741653153 \tabularnewline
20 & 465 & 448.911422711764 & 16.0885772882364 \tabularnewline
21 & 468 & 441.810832891377 & 26.1891671086232 \tabularnewline
22 & 467 & 439.782092942695 & 27.2179070573052 \tabularnewline
23 & 463 & 433.695873096649 & 29.304126903351 \tabularnewline
24 & 460 & 430.652763173626 & 29.3472368263739 \tabularnewline
25 & 462 & 427.609653250603 & 34.3903467493969 \tabularnewline
26 & 461 & 428.624023224944 & 32.3759767750559 \tabularnewline
27 & 476 & 461.083862403855 & 14.9161375961447 \tabularnewline
28 & 476 & 467.170082249901 & 8.82991775009886 \tabularnewline
29 & 471 & 466.15571227556 & 4.84428772443984 \tabularnewline
30 & 453 & 450.940162660446 & 2.05983733955445 \tabularnewline
31 & 443 & 439.782092942695 & 3.21790705730516 \tabularnewline
32 & 442 & 432.681503122308 & 9.31849687769198 \tabularnewline
33 & 444 & 433.695873096649 & 10.304126903351 \tabularnewline
34 & 438 & 427.609653250603 & 10.3903467493969 \tabularnewline
35 & 427 & 421.523433404557 & 5.4765665954427 \tabularnewline
36 & 424 & 418.480323481534 & 5.51967651846562 \tabularnewline
37 & 416 & 411.379733661148 & 4.62026633885244 \tabularnewline
38 & 406 & 409.350993712466 & -3.35099371246562 \tabularnewline
39 & 431 & 440.796462917036 & -9.7964629170358 \tabularnewline
40 & 434 & 446.882682763082 & -12.8826827630817 \tabularnewline
41 & 418 & 440.796462917036 & -22.7964629170358 \tabularnewline
42 & 412 & 431.667133147967 & -19.6671331479670 \tabularnewline
43 & 404 & 422.537803378898 & -18.5378033788983 \tabularnewline
44 & 409 & 421.523433404557 & -12.5234334045573 \tabularnewline
45 & 412 & 421.523433404557 & -9.5234334045573 \tabularnewline
46 & 406 & 417.465953507193 & -11.4659535071934 \tabularnewline
47 & 398 & 411.379733661148 & -13.3797336611476 \tabularnewline
48 & 397 & 409.350993712466 & -12.3509937124656 \tabularnewline
49 & 385 & 400.221663943397 & -15.2216639433968 \tabularnewline
50 & 390 & 403.26477386642 & -13.2647738664198 \tabularnewline
51 & 413 & 432.681503122308 & -19.681503122308 \tabularnewline
52 & 413 & 436.738983019672 & -23.7389830196719 \tabularnewline
53 & 401 & 433.695873096649 & -32.695873096649 \tabularnewline
54 & 397 & 425.580913301921 & -28.5809133019212 \tabularnewline
55 & 397 & 421.523433404557 & -24.5234334045573 \tabularnewline
56 & 409 & 423.552173353239 & -14.5521733532393 \tabularnewline
57 & 419 & 425.580913301921 & -6.5809133019212 \tabularnewline
58 & 424 & 425.580913301921 & -1.5809133019212 \tabularnewline
59 & 428 & 424.56654332758 & 3.43345667241977 \tabularnewline
60 & 430 & 423.552173353239 & 6.44782664676075 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58287&T=4

[TABLE]
[ROW][C]Multiple Linear Regression - Actuals, Interpolation, and Residuals[/C][/ROW]
[ROW][C]Time or Index[/C][C]Actuals[/C][C]InterpolationForecast[/C][C]ResidualsPrediction Error[/C][/ROW]
[ROW][C]1[/C][C]423[/C][C]430.652763173627[/C][C]-7.65276317362673[/C][/ROW]
[ROW][C]2[/C][C]427[/C][C]432.681503122308[/C][C]-5.68150312230792[/C][/ROW]
[ROW][C]3[/C][C]441[/C][C]470.213192172924[/C][C]-29.2131921729241[/C][/ROW]
[ROW][C]4[/C][C]449[/C][C]479.342521941993[/C][C]-30.3425219419928[/C][/ROW]
[ROW][C]5[/C][C]452[/C][C]478.328151967652[/C][C]-26.3281519676519[/C][/ROW]
[ROW][C]6[/C][C]462[/C][C]466.15571227556[/C][C]-4.15571227556016[/C][/ROW]
[ROW][C]7[/C][C]455[/C][C]456.01201253215[/C][C]-1.01201253215042[/C][/ROW]
[ROW][C]8[/C][C]461[/C][C]451.954532634787[/C][C]9.04546736521348[/C][/ROW]
[ROW][C]9[/C][C]461[/C][C]446.882682763082[/C][C]14.1173172369183[/C][/ROW]
[ROW][C]10[/C][C]463[/C][C]443.839572840059[/C][C]19.1604271599413[/C][/ROW]
[ROW][C]11[/C][C]462[/C][C]438.767722968354[/C][C]23.2322770316461[/C][/ROW]
[ROW][C]12[/C][C]456[/C][C]433.695873096649[/C][C]22.304126903351[/C][/ROW]
[ROW][C]13[/C][C]455[/C][C]428.624023224944[/C][C]26.3759767750559[/C][/ROW]
[ROW][C]14[/C][C]456[/C][C]429.638393199285[/C][C]26.3616068007149[/C][/ROW]
[ROW][C]15[/C][C]472[/C][C]466.15571227556[/C][C]5.84428772443984[/C][/ROW]
[ROW][C]16[/C][C]472[/C][C]474.270672070288[/C][C]-2.27067207028795[/C][/ROW]
[ROW][C]17[/C][C]471[/C][C]474.270672070288[/C][C]-3.27067207028795[/C][/ROW]
[ROW][C]18[/C][C]465[/C][C]464.126972326878[/C][C]0.87302767312179[/C][/ROW]
[ROW][C]19[/C][C]459[/C][C]453.983272583468[/C][C]5.01672741653153[/C][/ROW]
[ROW][C]20[/C][C]465[/C][C]448.911422711764[/C][C]16.0885772882364[/C][/ROW]
[ROW][C]21[/C][C]468[/C][C]441.810832891377[/C][C]26.1891671086232[/C][/ROW]
[ROW][C]22[/C][C]467[/C][C]439.782092942695[/C][C]27.2179070573052[/C][/ROW]
[ROW][C]23[/C][C]463[/C][C]433.695873096649[/C][C]29.304126903351[/C][/ROW]
[ROW][C]24[/C][C]460[/C][C]430.652763173626[/C][C]29.3472368263739[/C][/ROW]
[ROW][C]25[/C][C]462[/C][C]427.609653250603[/C][C]34.3903467493969[/C][/ROW]
[ROW][C]26[/C][C]461[/C][C]428.624023224944[/C][C]32.3759767750559[/C][/ROW]
[ROW][C]27[/C][C]476[/C][C]461.083862403855[/C][C]14.9161375961447[/C][/ROW]
[ROW][C]28[/C][C]476[/C][C]467.170082249901[/C][C]8.82991775009886[/C][/ROW]
[ROW][C]29[/C][C]471[/C][C]466.15571227556[/C][C]4.84428772443984[/C][/ROW]
[ROW][C]30[/C][C]453[/C][C]450.940162660446[/C][C]2.05983733955445[/C][/ROW]
[ROW][C]31[/C][C]443[/C][C]439.782092942695[/C][C]3.21790705730516[/C][/ROW]
[ROW][C]32[/C][C]442[/C][C]432.681503122308[/C][C]9.31849687769198[/C][/ROW]
[ROW][C]33[/C][C]444[/C][C]433.695873096649[/C][C]10.304126903351[/C][/ROW]
[ROW][C]34[/C][C]438[/C][C]427.609653250603[/C][C]10.3903467493969[/C][/ROW]
[ROW][C]35[/C][C]427[/C][C]421.523433404557[/C][C]5.4765665954427[/C][/ROW]
[ROW][C]36[/C][C]424[/C][C]418.480323481534[/C][C]5.51967651846562[/C][/ROW]
[ROW][C]37[/C][C]416[/C][C]411.379733661148[/C][C]4.62026633885244[/C][/ROW]
[ROW][C]38[/C][C]406[/C][C]409.350993712466[/C][C]-3.35099371246562[/C][/ROW]
[ROW][C]39[/C][C]431[/C][C]440.796462917036[/C][C]-9.7964629170358[/C][/ROW]
[ROW][C]40[/C][C]434[/C][C]446.882682763082[/C][C]-12.8826827630817[/C][/ROW]
[ROW][C]41[/C][C]418[/C][C]440.796462917036[/C][C]-22.7964629170358[/C][/ROW]
[ROW][C]42[/C][C]412[/C][C]431.667133147967[/C][C]-19.6671331479670[/C][/ROW]
[ROW][C]43[/C][C]404[/C][C]422.537803378898[/C][C]-18.5378033788983[/C][/ROW]
[ROW][C]44[/C][C]409[/C][C]421.523433404557[/C][C]-12.5234334045573[/C][/ROW]
[ROW][C]45[/C][C]412[/C][C]421.523433404557[/C][C]-9.5234334045573[/C][/ROW]
[ROW][C]46[/C][C]406[/C][C]417.465953507193[/C][C]-11.4659535071934[/C][/ROW]
[ROW][C]47[/C][C]398[/C][C]411.379733661148[/C][C]-13.3797336611476[/C][/ROW]
[ROW][C]48[/C][C]397[/C][C]409.350993712466[/C][C]-12.3509937124656[/C][/ROW]
[ROW][C]49[/C][C]385[/C][C]400.221663943397[/C][C]-15.2216639433968[/C][/ROW]
[ROW][C]50[/C][C]390[/C][C]403.26477386642[/C][C]-13.2647738664198[/C][/ROW]
[ROW][C]51[/C][C]413[/C][C]432.681503122308[/C][C]-19.681503122308[/C][/ROW]
[ROW][C]52[/C][C]413[/C][C]436.738983019672[/C][C]-23.7389830196719[/C][/ROW]
[ROW][C]53[/C][C]401[/C][C]433.695873096649[/C][C]-32.695873096649[/C][/ROW]
[ROW][C]54[/C][C]397[/C][C]425.580913301921[/C][C]-28.5809133019212[/C][/ROW]
[ROW][C]55[/C][C]397[/C][C]421.523433404557[/C][C]-24.5234334045573[/C][/ROW]
[ROW][C]56[/C][C]409[/C][C]423.552173353239[/C][C]-14.5521733532393[/C][/ROW]
[ROW][C]57[/C][C]419[/C][C]425.580913301921[/C][C]-6.5809133019212[/C][/ROW]
[ROW][C]58[/C][C]424[/C][C]425.580913301921[/C][C]-1.5809133019212[/C][/ROW]
[ROW][C]59[/C][C]428[/C][C]424.56654332758[/C][C]3.43345667241977[/C][/ROW]
[ROW][C]60[/C][C]430[/C][C]423.552173353239[/C][C]6.44782664676075[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58287&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58287&T=4

As an alternative you can also use a QR Code:  

The GUIDs for individual cells are displayed in the table below:

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
1423430.652763173627-7.65276317362673
2427432.681503122308-5.68150312230792
3441470.213192172924-29.2131921729241
4449479.342521941993-30.3425219419928
5452478.328151967652-26.3281519676519
6462466.15571227556-4.15571227556016
7455456.01201253215-1.01201253215042
8461451.9545326347879.04546736521348
9461446.88268276308214.1173172369183
10463443.83957284005919.1604271599413
11462438.76772296835423.2322770316461
12456433.69587309664922.304126903351
13455428.62402322494426.3759767750559
14456429.63839319928526.3616068007149
15472466.155712275565.84428772443984
16472474.270672070288-2.27067207028795
17471474.270672070288-3.27067207028795
18465464.1269723268780.87302767312179
19459453.9832725834685.01672741653153
20465448.91142271176416.0885772882364
21468441.81083289137726.1891671086232
22467439.78209294269527.2179070573052
23463433.69587309664929.304126903351
24460430.65276317362629.3472368263739
25462427.60965325060334.3903467493969
26461428.62402322494432.3759767750559
27476461.08386240385514.9161375961447
28476467.1700822499018.82991775009886
29471466.155712275564.84428772443984
30453450.9401626604462.05983733955445
31443439.7820929426953.21790705730516
32442432.6815031223089.31849687769198
33444433.69587309664910.304126903351
34438427.60965325060310.3903467493969
35427421.5234334045575.4765665954427
36424418.4803234815345.51967651846562
37416411.3797336611484.62026633885244
38406409.350993712466-3.35099371246562
39431440.796462917036-9.7964629170358
40434446.882682763082-12.8826827630817
41418440.796462917036-22.7964629170358
42412431.667133147967-19.6671331479670
43404422.537803378898-18.5378033788983
44409421.523433404557-12.5234334045573
45412421.523433404557-9.5234334045573
46406417.465953507193-11.4659535071934
47398411.379733661148-13.3797336611476
48397409.350993712466-12.3509937124656
49385400.221663943397-15.2216639433968
50390403.26477386642-13.2647738664198
51413432.681503122308-19.681503122308
52413436.738983019672-23.7389830196719
53401433.695873096649-32.695873096649
54397425.580913301921-28.5809133019212
55397421.523433404557-24.5234334045573
56409423.552173353239-14.5521733532393
57419425.580913301921-6.5809133019212
58424425.580913301921-1.5809133019212
59428424.566543327583.43345667241977
60430423.5521733532396.44782664676075







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
50.008028074103005640.01605614820601130.991971925896994
60.1198333170142390.2396666340284780.88016668298576
70.1254888902391680.2509777804783370.874511109760832
80.1953458153570370.3906916307140730.804654184642963
90.2400317843618040.4800635687236090.759968215638196
100.2804328363755950.5608656727511910.719567163624405
110.2942613649116670.5885227298233340.705738635088333
120.2520344473606240.5040688947212480.747965552639376
130.2206158603703850.441231720740770.779384139629615
140.1958540595529470.3917081191058940.804145940447053
150.2038887875995360.4077775751990720.796111212400464
160.1877355423691930.3754710847383870.812264457630807
170.1614218721041400.3228437442082800.83857812789586
180.1217693477907460.2435386955814920.878230652209254
190.08397628478404720.1679525695680940.916023715215953
200.06814541477203770.1362908295440750.931854585227962
210.07713979433357130.1542795886671430.922860205666429
220.08847713668148360.1769542733629670.911522863318516
230.10591572979980.21183145959960.8940842702002
240.132657788240830.265315576481660.86734221175917
250.2350483004065260.4700966008130510.764951699593474
260.4127927633458290.8255855266916570.587207236654171
270.4619787610278250.923957522055650.538021238972175
280.4688298114953440.9376596229906870.531170188504656
290.4400089704473820.8800179408947640.559991029552618
300.4082376497423180.8164752994846350.591762350257682
310.4285455849771970.8570911699543930.571454415022803
320.5002506445389140.9994987109221720.499749355461086
330.6035525234044070.7928949531911850.396447476595593
340.7338496075062920.5323007849874150.266150392493708
350.8277129966894250.3445740066211490.172287003310575
360.8958900043918940.2082199912162130.104109995608106
370.9401503820580670.1196992358838650.0598496179419327
380.9580781708471330.08384365830573440.0419218291528672
390.9564100897534580.08717982049308390.0435899102465419
400.9528965763757220.0942068472485570.0471034236242785
410.9584213628519750.083157274296050.041578637148025
420.9594646781503730.08107064369925380.0405353218496269
430.9595370128309880.08092597433802480.0404629871690124
440.9475010939105280.1049978121789450.0524989060894723
450.9293075617329350.1413848765341300.0706924382670648
460.9038058497818080.1923883004363830.0961941502181916
470.8717710245457230.2564579509085550.128228975454277
480.8252538282399410.3494923435201180.174746171760059
490.791912191936270.416175616127460.20808780806373
500.8079840057107560.3840319885784870.192015994289244
510.733180509750120.5336389804997590.266819490249879
520.6560100657100080.6879798685799840.343989934289992
530.6634393165949660.6731213668100680.336560683405034
540.8697691501221120.2604616997557750.130230849877888
550.8464351871179870.3071296257640250.153564812882013

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
5 & 0.00802807410300564 & 0.0160561482060113 & 0.991971925896994 \tabularnewline
6 & 0.119833317014239 & 0.239666634028478 & 0.88016668298576 \tabularnewline
7 & 0.125488890239168 & 0.250977780478337 & 0.874511109760832 \tabularnewline
8 & 0.195345815357037 & 0.390691630714073 & 0.804654184642963 \tabularnewline
9 & 0.240031784361804 & 0.480063568723609 & 0.759968215638196 \tabularnewline
10 & 0.280432836375595 & 0.560865672751191 & 0.719567163624405 \tabularnewline
11 & 0.294261364911667 & 0.588522729823334 & 0.705738635088333 \tabularnewline
12 & 0.252034447360624 & 0.504068894721248 & 0.747965552639376 \tabularnewline
13 & 0.220615860370385 & 0.44123172074077 & 0.779384139629615 \tabularnewline
14 & 0.195854059552947 & 0.391708119105894 & 0.804145940447053 \tabularnewline
15 & 0.203888787599536 & 0.407777575199072 & 0.796111212400464 \tabularnewline
16 & 0.187735542369193 & 0.375471084738387 & 0.812264457630807 \tabularnewline
17 & 0.161421872104140 & 0.322843744208280 & 0.83857812789586 \tabularnewline
18 & 0.121769347790746 & 0.243538695581492 & 0.878230652209254 \tabularnewline
19 & 0.0839762847840472 & 0.167952569568094 & 0.916023715215953 \tabularnewline
20 & 0.0681454147720377 & 0.136290829544075 & 0.931854585227962 \tabularnewline
21 & 0.0771397943335713 & 0.154279588667143 & 0.922860205666429 \tabularnewline
22 & 0.0884771366814836 & 0.176954273362967 & 0.911522863318516 \tabularnewline
23 & 0.1059157297998 & 0.2118314595996 & 0.8940842702002 \tabularnewline
24 & 0.13265778824083 & 0.26531557648166 & 0.86734221175917 \tabularnewline
25 & 0.235048300406526 & 0.470096600813051 & 0.764951699593474 \tabularnewline
26 & 0.412792763345829 & 0.825585526691657 & 0.587207236654171 \tabularnewline
27 & 0.461978761027825 & 0.92395752205565 & 0.538021238972175 \tabularnewline
28 & 0.468829811495344 & 0.937659622990687 & 0.531170188504656 \tabularnewline
29 & 0.440008970447382 & 0.880017940894764 & 0.559991029552618 \tabularnewline
30 & 0.408237649742318 & 0.816475299484635 & 0.591762350257682 \tabularnewline
31 & 0.428545584977197 & 0.857091169954393 & 0.571454415022803 \tabularnewline
32 & 0.500250644538914 & 0.999498710922172 & 0.499749355461086 \tabularnewline
33 & 0.603552523404407 & 0.792894953191185 & 0.396447476595593 \tabularnewline
34 & 0.733849607506292 & 0.532300784987415 & 0.266150392493708 \tabularnewline
35 & 0.827712996689425 & 0.344574006621149 & 0.172287003310575 \tabularnewline
36 & 0.895890004391894 & 0.208219991216213 & 0.104109995608106 \tabularnewline
37 & 0.940150382058067 & 0.119699235883865 & 0.0598496179419327 \tabularnewline
38 & 0.958078170847133 & 0.0838436583057344 & 0.0419218291528672 \tabularnewline
39 & 0.956410089753458 & 0.0871798204930839 & 0.0435899102465419 \tabularnewline
40 & 0.952896576375722 & 0.094206847248557 & 0.0471034236242785 \tabularnewline
41 & 0.958421362851975 & 0.08315727429605 & 0.041578637148025 \tabularnewline
42 & 0.959464678150373 & 0.0810706436992538 & 0.0405353218496269 \tabularnewline
43 & 0.959537012830988 & 0.0809259743380248 & 0.0404629871690124 \tabularnewline
44 & 0.947501093910528 & 0.104997812178945 & 0.0524989060894723 \tabularnewline
45 & 0.929307561732935 & 0.141384876534130 & 0.0706924382670648 \tabularnewline
46 & 0.903805849781808 & 0.192388300436383 & 0.0961941502181916 \tabularnewline
47 & 0.871771024545723 & 0.256457950908555 & 0.128228975454277 \tabularnewline
48 & 0.825253828239941 & 0.349492343520118 & 0.174746171760059 \tabularnewline
49 & 0.79191219193627 & 0.41617561612746 & 0.20808780806373 \tabularnewline
50 & 0.807984005710756 & 0.384031988578487 & 0.192015994289244 \tabularnewline
51 & 0.73318050975012 & 0.533638980499759 & 0.266819490249879 \tabularnewline
52 & 0.656010065710008 & 0.687979868579984 & 0.343989934289992 \tabularnewline
53 & 0.663439316594966 & 0.673121366810068 & 0.336560683405034 \tabularnewline
54 & 0.869769150122112 & 0.260461699755775 & 0.130230849877888 \tabularnewline
55 & 0.846435187117987 & 0.307129625764025 & 0.153564812882013 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58287&T=5

[TABLE]
[ROW][C]Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]p-values[/C][C]Alternative Hypothesis[/C][/ROW]
[ROW][C]breakpoint index[/C][C]greater[/C][C]2-sided[/C][C]less[/C][/ROW]
[ROW][C]5[/C][C]0.00802807410300564[/C][C]0.0160561482060113[/C][C]0.991971925896994[/C][/ROW]
[ROW][C]6[/C][C]0.119833317014239[/C][C]0.239666634028478[/C][C]0.88016668298576[/C][/ROW]
[ROW][C]7[/C][C]0.125488890239168[/C][C]0.250977780478337[/C][C]0.874511109760832[/C][/ROW]
[ROW][C]8[/C][C]0.195345815357037[/C][C]0.390691630714073[/C][C]0.804654184642963[/C][/ROW]
[ROW][C]9[/C][C]0.240031784361804[/C][C]0.480063568723609[/C][C]0.759968215638196[/C][/ROW]
[ROW][C]10[/C][C]0.280432836375595[/C][C]0.560865672751191[/C][C]0.719567163624405[/C][/ROW]
[ROW][C]11[/C][C]0.294261364911667[/C][C]0.588522729823334[/C][C]0.705738635088333[/C][/ROW]
[ROW][C]12[/C][C]0.252034447360624[/C][C]0.504068894721248[/C][C]0.747965552639376[/C][/ROW]
[ROW][C]13[/C][C]0.220615860370385[/C][C]0.44123172074077[/C][C]0.779384139629615[/C][/ROW]
[ROW][C]14[/C][C]0.195854059552947[/C][C]0.391708119105894[/C][C]0.804145940447053[/C][/ROW]
[ROW][C]15[/C][C]0.203888787599536[/C][C]0.407777575199072[/C][C]0.796111212400464[/C][/ROW]
[ROW][C]16[/C][C]0.187735542369193[/C][C]0.375471084738387[/C][C]0.812264457630807[/C][/ROW]
[ROW][C]17[/C][C]0.161421872104140[/C][C]0.322843744208280[/C][C]0.83857812789586[/C][/ROW]
[ROW][C]18[/C][C]0.121769347790746[/C][C]0.243538695581492[/C][C]0.878230652209254[/C][/ROW]
[ROW][C]19[/C][C]0.0839762847840472[/C][C]0.167952569568094[/C][C]0.916023715215953[/C][/ROW]
[ROW][C]20[/C][C]0.0681454147720377[/C][C]0.136290829544075[/C][C]0.931854585227962[/C][/ROW]
[ROW][C]21[/C][C]0.0771397943335713[/C][C]0.154279588667143[/C][C]0.922860205666429[/C][/ROW]
[ROW][C]22[/C][C]0.0884771366814836[/C][C]0.176954273362967[/C][C]0.911522863318516[/C][/ROW]
[ROW][C]23[/C][C]0.1059157297998[/C][C]0.2118314595996[/C][C]0.8940842702002[/C][/ROW]
[ROW][C]24[/C][C]0.13265778824083[/C][C]0.26531557648166[/C][C]0.86734221175917[/C][/ROW]
[ROW][C]25[/C][C]0.235048300406526[/C][C]0.470096600813051[/C][C]0.764951699593474[/C][/ROW]
[ROW][C]26[/C][C]0.412792763345829[/C][C]0.825585526691657[/C][C]0.587207236654171[/C][/ROW]
[ROW][C]27[/C][C]0.461978761027825[/C][C]0.92395752205565[/C][C]0.538021238972175[/C][/ROW]
[ROW][C]28[/C][C]0.468829811495344[/C][C]0.937659622990687[/C][C]0.531170188504656[/C][/ROW]
[ROW][C]29[/C][C]0.440008970447382[/C][C]0.880017940894764[/C][C]0.559991029552618[/C][/ROW]
[ROW][C]30[/C][C]0.408237649742318[/C][C]0.816475299484635[/C][C]0.591762350257682[/C][/ROW]
[ROW][C]31[/C][C]0.428545584977197[/C][C]0.857091169954393[/C][C]0.571454415022803[/C][/ROW]
[ROW][C]32[/C][C]0.500250644538914[/C][C]0.999498710922172[/C][C]0.499749355461086[/C][/ROW]
[ROW][C]33[/C][C]0.603552523404407[/C][C]0.792894953191185[/C][C]0.396447476595593[/C][/ROW]
[ROW][C]34[/C][C]0.733849607506292[/C][C]0.532300784987415[/C][C]0.266150392493708[/C][/ROW]
[ROW][C]35[/C][C]0.827712996689425[/C][C]0.344574006621149[/C][C]0.172287003310575[/C][/ROW]
[ROW][C]36[/C][C]0.895890004391894[/C][C]0.208219991216213[/C][C]0.104109995608106[/C][/ROW]
[ROW][C]37[/C][C]0.940150382058067[/C][C]0.119699235883865[/C][C]0.0598496179419327[/C][/ROW]
[ROW][C]38[/C][C]0.958078170847133[/C][C]0.0838436583057344[/C][C]0.0419218291528672[/C][/ROW]
[ROW][C]39[/C][C]0.956410089753458[/C][C]0.0871798204930839[/C][C]0.0435899102465419[/C][/ROW]
[ROW][C]40[/C][C]0.952896576375722[/C][C]0.094206847248557[/C][C]0.0471034236242785[/C][/ROW]
[ROW][C]41[/C][C]0.958421362851975[/C][C]0.08315727429605[/C][C]0.041578637148025[/C][/ROW]
[ROW][C]42[/C][C]0.959464678150373[/C][C]0.0810706436992538[/C][C]0.0405353218496269[/C][/ROW]
[ROW][C]43[/C][C]0.959537012830988[/C][C]0.0809259743380248[/C][C]0.0404629871690124[/C][/ROW]
[ROW][C]44[/C][C]0.947501093910528[/C][C]0.104997812178945[/C][C]0.0524989060894723[/C][/ROW]
[ROW][C]45[/C][C]0.929307561732935[/C][C]0.141384876534130[/C][C]0.0706924382670648[/C][/ROW]
[ROW][C]46[/C][C]0.903805849781808[/C][C]0.192388300436383[/C][C]0.0961941502181916[/C][/ROW]
[ROW][C]47[/C][C]0.871771024545723[/C][C]0.256457950908555[/C][C]0.128228975454277[/C][/ROW]
[ROW][C]48[/C][C]0.825253828239941[/C][C]0.349492343520118[/C][C]0.174746171760059[/C][/ROW]
[ROW][C]49[/C][C]0.79191219193627[/C][C]0.41617561612746[/C][C]0.20808780806373[/C][/ROW]
[ROW][C]50[/C][C]0.807984005710756[/C][C]0.384031988578487[/C][C]0.192015994289244[/C][/ROW]
[ROW][C]51[/C][C]0.73318050975012[/C][C]0.533638980499759[/C][C]0.266819490249879[/C][/ROW]
[ROW][C]52[/C][C]0.656010065710008[/C][C]0.687979868579984[/C][C]0.343989934289992[/C][/ROW]
[ROW][C]53[/C][C]0.663439316594966[/C][C]0.673121366810068[/C][C]0.336560683405034[/C][/ROW]
[ROW][C]54[/C][C]0.869769150122112[/C][C]0.260461699755775[/C][C]0.130230849877888[/C][/ROW]
[ROW][C]55[/C][C]0.846435187117987[/C][C]0.307129625764025[/C][C]0.153564812882013[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58287&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58287&T=5

As an alternative you can also use a QR Code:  

The GUIDs for individual cells are displayed in the table below:

Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
50.008028074103005640.01605614820601130.991971925896994
60.1198333170142390.2396666340284780.88016668298576
70.1254888902391680.2509777804783370.874511109760832
80.1953458153570370.3906916307140730.804654184642963
90.2400317843618040.4800635687236090.759968215638196
100.2804328363755950.5608656727511910.719567163624405
110.2942613649116670.5885227298233340.705738635088333
120.2520344473606240.5040688947212480.747965552639376
130.2206158603703850.441231720740770.779384139629615
140.1958540595529470.3917081191058940.804145940447053
150.2038887875995360.4077775751990720.796111212400464
160.1877355423691930.3754710847383870.812264457630807
170.1614218721041400.3228437442082800.83857812789586
180.1217693477907460.2435386955814920.878230652209254
190.08397628478404720.1679525695680940.916023715215953
200.06814541477203770.1362908295440750.931854585227962
210.07713979433357130.1542795886671430.922860205666429
220.08847713668148360.1769542733629670.911522863318516
230.10591572979980.21183145959960.8940842702002
240.132657788240830.265315576481660.86734221175917
250.2350483004065260.4700966008130510.764951699593474
260.4127927633458290.8255855266916570.587207236654171
270.4619787610278250.923957522055650.538021238972175
280.4688298114953440.9376596229906870.531170188504656
290.4400089704473820.8800179408947640.559991029552618
300.4082376497423180.8164752994846350.591762350257682
310.4285455849771970.8570911699543930.571454415022803
320.5002506445389140.9994987109221720.499749355461086
330.6035525234044070.7928949531911850.396447476595593
340.7338496075062920.5323007849874150.266150392493708
350.8277129966894250.3445740066211490.172287003310575
360.8958900043918940.2082199912162130.104109995608106
370.9401503820580670.1196992358838650.0598496179419327
380.9580781708471330.08384365830573440.0419218291528672
390.9564100897534580.08717982049308390.0435899102465419
400.9528965763757220.0942068472485570.0471034236242785
410.9584213628519750.083157274296050.041578637148025
420.9594646781503730.08107064369925380.0405353218496269
430.9595370128309880.08092597433802480.0404629871690124
440.9475010939105280.1049978121789450.0524989060894723
450.9293075617329350.1413848765341300.0706924382670648
460.9038058497818080.1923883004363830.0961941502181916
470.8717710245457230.2564579509085550.128228975454277
480.8252538282399410.3494923435201180.174746171760059
490.791912191936270.416175616127460.20808780806373
500.8079840057107560.3840319885784870.192015994289244
510.733180509750120.5336389804997590.266819490249879
520.6560100657100080.6879798685799840.343989934289992
530.6634393165949660.6731213668100680.336560683405034
540.8697691501221120.2604616997557750.130230849877888
550.8464351871179870.3071296257640250.153564812882013







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level10.0196078431372549OK
10% type I error level70.137254901960784NOK

\begin{tabular}{lllllllll}
\hline
Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
Description & # significant tests & % significant tests & OK/NOK \tabularnewline
1% type I error level & 0 & 0 & OK \tabularnewline
5% type I error level & 1 & 0.0196078431372549 & OK \tabularnewline
10% type I error level & 7 & 0.137254901960784 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58287&T=6

[TABLE]
[ROW][C]Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]Description[/C][C]# significant tests[/C][C]% significant tests[/C][C]OK/NOK[/C][/ROW]
[ROW][C]1% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]1[/C][C]0.0196078431372549[/C][C]OK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]7[/C][C]0.137254901960784[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58287&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58287&T=6

As an alternative you can also use a QR Code:  

The GUIDs for individual cells are displayed in the table below:

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level10.0196078431372549OK
10% type I error level70.137254901960784NOK



Parameters (Session):
par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
Parameters (R input):
par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
R code (references can be found in the software module):
library(lattice)
library(lmtest)
n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
par1 <- as.numeric(par1)
x <- t(y)
k <- length(x[1,])
n <- length(x[,1])
x1 <- cbind(x[,par1], x[,1:k!=par1])
mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
colnames(x1) <- mycolnames #colnames(x)[par1]
x <- x1
if (par3 == 'First Differences'){
x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))
for (i in 1:n-1) {
for (j in 1:k) {
x2[i,j] <- x[i+1,j] - x[i,j]
}
}
x <- x2
}
if (par2 == 'Include Monthly Dummies'){
x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
for (i in 1:11){
x2[seq(i,n,12),i] <- 1
}
x <- cbind(x, x2)
}
if (par2 == 'Include Quarterly Dummies'){
x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
for (i in 1:3){
x2[seq(i,n,4),i] <- 1
}
x <- cbind(x, x2)
}
k <- length(x[1,])
if (par3 == 'Linear Trend'){
x <- cbind(x, c(1:n))
colnames(x)[k+1] <- 't'
}
x
k <- length(x[1,])
df <- as.data.frame(x)
(mylm <- lm(df))
(mysum <- summary(mylm))
if (n > n25) {
kp3 <- k + 3
nmkm3 <- n - k - 3
gqarr <- array(NA, dim=c(nmkm3-kp3+1,3))
numgqtests <- 0
numsignificant1 <- 0
numsignificant5 <- 0
numsignificant10 <- 0
for (mypoint in kp3:nmkm3) {
j <- 0
numgqtests <- numgqtests + 1
for (myalt in c('greater', 'two.sided', 'less')) {
j <- j + 1
gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value
}
if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1
if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1
if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1
}
gqarr
}
bitmap(file='test0.png')
plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')
points(x[,1]-mysum$resid)
grid()
dev.off()
bitmap(file='test1.png')
plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
grid()
dev.off()
bitmap(file='test2.png')
hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
grid()
dev.off()
bitmap(file='test3.png')
densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
dev.off()
bitmap(file='test4.png')
qqnorm(mysum$resid, main='Residual Normal Q-Q Plot')
qqline(mysum$resid)
grid()
dev.off()
(myerror <- as.ts(mysum$resid))
bitmap(file='test5.png')
dum <- cbind(lag(myerror,k=1),myerror)
dum
dum1 <- dum[2:length(myerror),]
dum1
z <- as.data.frame(dum1)
z
plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals')
lines(lowess(z))
abline(lm(z))
grid()
dev.off()
bitmap(file='test6.png')
acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
grid()
dev.off()
bitmap(file='test7.png')
pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
grid()
dev.off()
bitmap(file='test8.png')
opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
plot(mylm, las = 1, sub='Residual Diagnostics')
par(opar)
dev.off()
if (n > n25) {
bitmap(file='test9.png')
plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
grid()
dev.off()
}
load(file='createtable')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
a<-table.row.end(a)
myeq <- colnames(x)[1]
myeq <- paste(myeq, '[t] = ', sep='')
for (i in 1:k){
if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
myeq <- paste(myeq, mysum$coefficients[i,1], sep=' ')
if (rownames(mysum$coefficients)[i] != '(Intercept)') {
myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='')
if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
}
}
myeq <- paste(myeq, ' + e[t]')
a<-table.row.start(a)
a<-table.element(a, myeq)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable1.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,hyperlink('ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Variable',header=TRUE)
a<-table.element(a,'Parameter',header=TRUE)
a<-table.element(a,'S.D.',header=TRUE)
a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
a<-table.element(a,'2-tail p-value',header=TRUE)
a<-table.element(a,'1-tail p-value',header=TRUE)
a<-table.row.end(a)
for (i in 1:k){
a<-table.row.start(a)
a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE)
a<-table.element(a,mysum$coefficients[i,1])
a<-table.element(a, round(mysum$coefficients[i,2],6))
a<-table.element(a, round(mysum$coefficients[i,3],4))
a<-table.element(a, round(mysum$coefficients[i,4],6))
a<-table.element(a, round(mysum$coefficients[i,4]/2,6))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable2.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple R',1,TRUE)
a<-table.element(a, sqrt(mysum$r.squared))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'R-squared',1,TRUE)
a<-table.element(a, mysum$r.squared)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Adjusted R-squared',1,TRUE)
a<-table.element(a, mysum$adj.r.squared)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (value)',1,TRUE)
a<-table.element(a, mysum$fstatistic[1])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
a<-table.element(a, mysum$fstatistic[2])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)
a<-table.element(a, mysum$fstatistic[3])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'p-value',1,TRUE)
a<-table.element(a, 1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Residual Standard Deviation',1,TRUE)
a<-table.element(a, mysum$sigma)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
a<-table.element(a, sum(myerror*myerror))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable3.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Time or Index', 1, TRUE)
a<-table.element(a, 'Actuals', 1, TRUE)
a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
a<-table.row.end(a)
for (i in 1:n) {
a<-table.row.start(a)
a<-table.element(a,i, 1, TRUE)
a<-table.element(a,x[i])
a<-table.element(a,x[i]-mysum$resid[i])
a<-table.element(a,mysum$resid[i])
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable4.tab')
if (n > n25) {
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'p-values',header=TRUE)
a<-table.element(a,'Alternative Hypothesis',3,header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'breakpoint index',header=TRUE)
a<-table.element(a,'greater',header=TRUE)
a<-table.element(a,'2-sided',header=TRUE)
a<-table.element(a,'less',header=TRUE)
a<-table.row.end(a)
for (mypoint in kp3:nmkm3) {
a<-table.row.start(a)
a<-table.element(a,mypoint,header=TRUE)
a<-table.element(a,gqarr[mypoint-kp3+1,1])
a<-table.element(a,gqarr[mypoint-kp3+1,2])
a<-table.element(a,gqarr[mypoint-kp3+1,3])
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable5.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Description',header=TRUE)
a<-table.element(a,'# significant tests',header=TRUE)
a<-table.element(a,'% significant tests',header=TRUE)
a<-table.element(a,'OK/NOK',header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'1% type I error level',header=TRUE)
a<-table.element(a,numsignificant1)
a<-table.element(a,numsignificant1/numgqtests)
if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'5% type I error level',header=TRUE)
a<-table.element(a,numsignificant5)
a<-table.element(a,numsignificant5/numgqtests)
if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'10% type I error level',header=TRUE)
a<-table.element(a,numsignificant10)
a<-table.element(a,numsignificant10/numgqtests)
if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable6.tab')
}